The goal of this project is to develop mathematical tools to model biochemical networks from experimental data. These tools will then be applied to transcriptomics, proteomics, and metabolomics data collected from a network of ten genes of the model organism Saccharomyces cerevisiae that are involved in oxidative stress response. [unreadable] [unreadable] The proposed approach is to develop a two-stage modeling process. Viewing a biochemical network as a continuous dynamical system, described by a collection of ordinary differential equations, we first approximate the system by a discrete-time system, using numerical methods. Several tools are then used to analyze this discrete system. One of these tools is a further reduction, achieved by categorizing the state values of the variables into a finite set of states. The resulting framework allows the use of well-developed methods from computational algebra to study the whole space of possible models for the discrete-time system on a finite state set. [unreadable] [unreadable] To demonstrate and test the mathematical methodologies, experiments will be carded out with the baker's yeast Saccharomyces cerevisiae. Cell cultures of wild type yeast will be perturbed by addition of cumene hydroperoxide, and oxidative stress, and samples will be measured in a time course. The experiment will be repeated ten times, each time with a different gene deletion mutant. Mutants will be selected according to the hypotheses generated by the mathematical models. In addition to supporting the mathematical developments, the experiments are expected to reveal details about the regulation of glutathione metabolism and related antioxidant molecules (such as Lascorbic acid). [unreadable] [unreadable] [unreadable]